Unsupervised deep neuron-per-neuron hashing
Sign Up to like & getrecommendations! Published in 2018 at "Applied Intelligence"
DOI: 10.1007/s10489-018-1353-5
Abstract: Hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. A variety of hashing methods have been developed for learning an efficient binary data representation, mainly by relaxing some imposed constraints… read more here.
Keywords: neuron; neuron hashing; deep neuron; unsupervised deep ... See more keywords
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons
Sign Up to like & getrecommendations! Published in 2021 at "Nature Communications"
DOI: 10.1038/s41467-021-26751-5
Abstract: In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in… read more here.
Keywords: identifies semantic; deep learning; face; learning identifies ... See more keywords
High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.
Sign Up to like & getrecommendations! Published in 2023 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.2213149120
Abstract: Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual… read more here.
Keywords: iterative subtomogram; unsupervised deep; deep iterative; high throughput ... See more keywords
Unsupervised Deep Learning Can Identify Protein Functional Groups from Unaligned Sequences
Sign Up to like & getrecommendations! Published in 2023 at "Genome Biology and Evolution"
DOI: 10.1093/gbe/evad084
Abstract: Abstract Interpreting protein function from sequence data is a fundamental goal of bioinformatics. However, our current understanding of protein diversity is bottlenecked by the fact that most proteins have only been functionally validated in model… read more here.
Keywords: unsupervised deep; learning identify; learning; deep learning ... See more keywords
Unsupervised Deep Learning to Detect Agitation from Videos in People with Dementia
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2022.3143990
Abstract: Behavioural symptoms of dementia present a significant risk within Long Term Care (LTC) homes, which face difficulties supporting residents and monitoring their safety with limited staffing resources. Many LTC facilities have installed video surveillance systems… read more here.
Keywords: detect agitation; unsupervised deep; people dementia; deep learning ... See more keywords
Unsupervised Deep-Learning-Based Reconfigurable Intelligent Surface-Aided Broadcasting Communications in Industrial IoTs
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3169276
Abstract: This article presents a general system framework that lays the foundation for reconfigurable intelligent surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in… read more here.
Keywords: unsupervised deep; intelligent surface; reconfigurable intelligent; deep learning ... See more keywords
Hybrid Beamforming Based on an Unsupervised Deep Learning Network for Downlink Channels With Imperfect CSI
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3179362
Abstract: Hybrid beamforming can provide rapid data transmission rates while reducing the complexity and cost of massive multiple-input multiple-output (MIMO) systems. However, channel state information (CSI) is imperfect in realistic downlink channels, introducing challenges to hybrid… read more here.
Keywords: unsupervised deep; imperfect csi; deep learning; downlink channels ... See more keywords
Unsupervised Deep Background Matting Using Deep Matte Prior
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2021.3132461
Abstract: Background matting is a recently developed image matting approach, with applications to image and video editing. It refers to estimating both the alpha matte and foreground from a pair of images with and without foreground… read more here.
Keywords: deep matte; unsupervised deep; alpha matte; background matting ... See more keywords
An Unsupervised Deep Neural Network Approach Based on Ensemble Learning to Suppress Seismic Surface-Related Multiples
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3225809
Abstract: Surface-related multiples are generally removed as noise. To suppress surface-related multiples, we propose an unsupervised deep neural network approach based on ensemble learning (UDNNEL). The unsupervised deep neural network (UDNN) has excellent nonlinear mapping ability,… read more here.
Keywords: neural network; unsupervised deep; deep neural; surface ... See more keywords
LOUD: Local Orthogonalization-Constrained Unsupervised Deep-Learning Denoiser
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3226404
Abstract: Random noise attenuation of seismic data is a fundamental problem in seismic data processing. It is not only an important problem itself, but also a crucial step for the subsequent tasks, for example, migration and… read more here.
Keywords: unsupervised deep; local orthogonalization; orthogonalization constrained; orthogonalization ... See more keywords
Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2017.2781422
Abstract: In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently,… read more here.
Keywords: pseudo labels; pseudo; image; deep hashing ... See more keywords